research-article

A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems

Authors:

Oscar Castillo,

Leticia Amador-Angulo,

Juan R. Castro,

Mario Garcia-ValdezAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 354, Issue C

Pages 257 - 274

https://doi.org/10.1016/j.ins.2016.03.026

Published: 01 August 2016 Publication History

Abstract

This paper presents a comparative study of type-2 fuzzy logic systems with respect to interval type-2 and type-1 fuzzy logic systems to show the efficiency and performance of a generalized type-2 fuzzy logic controller (GT2FLC). We used different types of fuzzy logic systems for designing the fuzzy controllers of complex non-linear plants. The theory of alpha planes is used for approximating generalized type-2 fuzzy logic in fuzzy controllers. In the defuzzification process, the Karnik and Mendel Algorithm is used. Simulation results with a type-1 fuzzy logic controller (T1FLC), an interval type-2 fuzzy logic controller (IT2FLC) and with a generalized type-2 fuzzy logic controller (GT2FLC) for benchmark plants are presented. The advantage of using generalized type-2 fuzzy logic in fuzzy controllers is verified with four benchmark problems. We considered different levels of noise, number of alpha planes and four types of membership functions in the simulations for comparison and to analyze the approach of generalized type-2 fuzzy logic systems when applied in fuzzy control.

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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 354, Issue C

August 2016

301 pages

ISSN:0020-0255

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Copyright © Elsevier Inc.

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Elsevier Science Inc.

United States

Publication History

Published: 01 August 2016

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https://dl.acm.org/doi/10.1109/TFUZZ.2024.3429493
Wang EChen XLi YFu ZHuang J(2024)Lower Limb Motion Intent Recognition Based on Sensor Fusion and Fuzzy Multitask LearningIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.336438232:5(2903-2914)Online publication date: 9-Feb-2024
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